Radiographic scoliosis angle estimation: spline-based measurement reveals superior reliability compared to traditional COBB method.

Introduction and Objective: Although being standard for scoliosis curve size estimation, COBB angle measurement is well known to be inaccurate, due to a high interobserver variance in end vertebra selection and end plate contour delineation. We propose a stepwise improvement by using a spline constructed from vertebra centroids to resemble spinal curve characteristics more closely. To enhance precision even further, a neural net was trained to detect the centroids automatically.
Materials & Methods: Vertebra centroids in AP spinal X-ray images of varying quality from 551 scoliosis patients were manually labeled by 4 investigators. With these inputs, splines were generated and the computed curve sizes were compared to the manually measured COBB angles and to the curve estimation obtained from the neural net.
Results: Splines achieved a higher interobserver correlation of 0.92-0.95 compared to manual COBB measurements (0.83-0.92) and showed 1.5-2 times less variance, depending on the anatomic region. This translates into an average of 1° of interobserver measurement deviation for spline-based curve estimation compared to 3°-8° for COBB measurements. The neural net was even more precise and achieved mean deviations below 0.5°.
Conclusion: In conclusion, our data suggest an advantage of spline-based automated measuring systems, so further investigations are warranted to abandon manual COBB measurements.